Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	24,283
2	Arizona	23,130
3	New York	21,502
4	Florida	21,017
5	New Jersey	20,543
6	Mississippi	18,750
7	Rhode Island	17,746
8	Alabama	17,087
9	District of Columbia	17,001
10	Massachusetts	16,929
11	South Carolina	16,673
12	Georgia	15,403
13	Delaware	14,995
14	Nevada	14,913
15	Texas	14,547
16	Tennessee	14,371
17	Maryland	14,364
18	Illinois	13,965
19	Connecticut	13,895
20	Iowa	13,826
21	Arkansas	13,575
22	Nebraska	13,206
23	California	12,346
24	Utah	12,270
25	North Carolina	11,260
26	Idaho	11,085
27	Virginia	10,309
28	Indiana	9,791
29	South Dakota	9,767
30	New Mexico	9,603
31	Wisconsin	9,466
32	Minnesota	9,395
33	Kansas	9,328
34	Pennsylvania	8,981
35	Michigan	8,914
36	Oklahoma	8,745
37	North Dakota	8,176
38	Colorado	7,967
39	Missouri	7,769
40	Washington	7,524
41	Ohio	7,519
42	Kentucky	6,598
43	Puerto Rico	5,028
44	New Hampshire	4,789
45	Alaska	4,702
46	Wyoming	4,540
47	Oregon	4,221
48	West Virginia	3,529
49	Montana	3,457
50	Maine	2,876
51	Vermont	2,253
52	Hawaii	1,300

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	427
2	Mississippi	392
3	Louisiana	372
4	Tennessee	332
5	Nevada	326
6	Georgia	316
7	Alabama	305
8	South Carolina	300
9	Texas	299
10	Arizona	289
11	Idaho	288
12	Oklahoma	279
13	Arkansas	259
14	California	240
15	Missouri	228
16	Rhode Island	181
17	Kansas	174
18	New Mexico	172
19	North Carolina	165
20	Nebraska	160
21	Alaska	154
22	North Dakota	153
23	Utah	142
24	Maryland	140
25	Wisconsin	139
26	Kentucky	134
27	Iowa	133
28	Virginia	133
29	Washington	125
30	Montana	110
31	Ohio	108
32	Delaware	106
33	Minnesota	106
34	District of Columbia	103
35	Illinois	102
36	Indiana	100
37	Puerto Rico	95
38	South Dakota	92
39	Colorado	88
40	Wyoming	88
41	Pennsylvania	83
42	Oregon	82
43	Michigan	75
44	Connecticut	71
45	West Virginia	68
46	Massachusetts	50
47	New Jersey	44
48	Hawaii	42
49	New York	31
50	New Hampshire	18
51	Maine	12
52	Vermont	3

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,778
2	New York	1,662
3	Massachusetts	1,244
4	Connecticut	1,241
5	Rhode Island	950
6	Louisiana	835
7	District of Columbia	827
8	Michigan	643
9	Illinois	604
10	Delaware	596
11	Maryland	575
12	Pennsylvania	563
13	Mississippi	525
14	Arizona	476
15	Indiana	435
16	Georgia	335
17	Colorado	317
18	Alabama	313
19	South Carolina	313
20	New Hampshire	302
21	New Mexico	301
22	Florida	294
23	Ohio	292
24	Minnesota	288
25	Iowa	270
26	Nevada	253
27	Virginia	248
28	Texas	235
29	California	225
30	Washington	215
31	Missouri	206
32	North Carolina	180
33	Nebraska	171
34	Kentucky	166
35	Wisconsin	158
36	Tennessee	147
37	South Dakota	145
38	Arkansas	143
39	North Dakota	139
40	Oklahoma	132
41	Kansas	119
42	Idaho	98
43	Utah	92
44	Maine	90
45	Vermont	89
46	Oregon	74
47	Puerto Rico	66
48	West Virginia	62
49	Montana	50
50	Wyoming	44
51	Alaska	27
52	Hawaii	17

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Texas	20
2	Louisiana	8
3	South Carolina	8
4	Arizona	7
5	Florida	7
6	Mississippi	7
7	Nevada	5
8	Alabama	4
9	Georgia	4
10	Idaho	4
11	Arkansas	3
12	California	3
13	Ohio	3
14	Iowa	2
15	Kentucky	2
16	Maryland	2
17	Massachusetts	2
18	Missouri	2
19	Montana	2
20	New Mexico	2
21	North Carolina	2
22	Oklahoma	2
23	South Dakota	2
24	Tennessee	2
25	Washington	2
26	Colorado	1
27	Connecticut	1
28	District of Columbia	1
29	Illinois	1
30	Indiana	1
31	Kansas	1
32	Nebraska	1
33	North Dakota	1
34	Oregon	1
35	Pennsylvania	1
36	Puerto Rico	1
37	Rhode Island	1
38	Utah	1
39	Virginia	1
40	West Virginia	1
41	Wisconsin	1
42	Alaska	0
43	Delaware	0
44	Hawaii	0
45	Maine	0
46	Michigan	0
47	Minnesota	0
48	New Hampshire	0
49	New Jersey	0
50	New York	0
51	Vermont	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	138,692	1	99
Lake	Tennessee	102,908	2	99
Lee	Arkansas	99,131	3	99
Dakota	Nebraska	94,477	4	99
Buena Vista	Iowa	90,673	5	99
Davidson	Tennessee	29,557	113	96
Richland	South Carolina	17,787	382	87
Orange	California	11,107	850	72
York	South Carolina	10,577	891	71
Pierce	Washington	5,689	1602	49

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	4,020	1	99
Randolph	Georgia	3,836	2	99
Terrell	Georgia	3,399	3	99
Early	Georgia	3,042	4	99
McKinley	New Mexico	3,041	5	99
Richland	South Carolina	308	676	78
Davidson	Tennessee	279	740	76
Orange	California	185	1016	67
Pierce	Washington	147	1179	62
York	South Carolina	93	1505	52

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.